Multi-Mode Instance-Intensive Workflow Task Batch Scheduling in Containerized Hybrid Cloud

Abstract

The migration of containerized microservices from virtual machines (VMs) to cloud data centers has become the most advanced deployment technique for large software applications in the cloud. This study investigates the scheduling of instance-intensive workflow (IWF) tasks to be executed in containers on a hybrid cloud when computational resources are limited. The process of scheduling these IWF tasks becomes complicated when considering the deployment time of containers, inter-task communication time, and their dependencies simultaneously, particularly when the task can choose multi-mode executions due to the flexible computational resource allocation of the container. We propose a batch scheduling strategy (BSS) for the IWF task scheduling problem. The BSS prioritizes the execution of IWF tasks with high repetition rates with a certain probability and records the virtual machines and modes selected for task execution, which can reduce the data transfer time and the randomness of computation. Based on this, we use an improved hybrid algorithm combined with BSS to solve the multi-mode IWF task scheduling problem. The experimental results demonstrate that employing the BSS can reduce the scheduling time by 6% when the number of workflows increases to 80. Additionally, we tested the effectiveness of all operators in the algorithm, and the results show that each step of the algorithm yields good performance. Compared to similar algorithms in related studies, the overall algorithm can achieve a maximum reduction of approximately 18% in the target value.